Algebraic Operations on Melodies

May not music be described as the mathematics of the sense, mathematics as music of the reason?

—James Joseph Sylvester

I have always been very drawn to the intersection of math and music. During the summer of 2015, I attended the Mathematica Summer Camp (which was very fun, by the way) and had to complete a programming project in Mathematica over the course of two weeks. Naturally, I wanted to do something with math and music.

I eventually settled on a random song generator, although with a little bit of a twist. Instead of generating a random melody, the generator will take two input melodies and apply a random set of operations to them, resulting in a unique song. In other words, the source of randomness is not in the melodies itself, but in how they are transformed, repeated, and layered over themselves.

Algebraic Operations on Melodies Wolfram Demonstration.

Algebraic Operations on Melodies Wolfram Demonstration.

Source: Wolfram Demonstrations Project.

I am pretty happy with the result, but due to time constraints I couldn’t make it as grand as I would have liked (and I haven’t worked on it since then). For instance, one thing that the program currently lacks is the ability to input a custom melody—instead, it relies on a few built-in ones. To be truly viable as a composition tool, I would most certainly need to include custom melodies, among other things.

And so, without further ado, I present to you my algebraic music generator.

Regardless of the fact that my program is of dubious flexibility and utility, I find the mathematics behind it really interesting, so in this post I will focus on the math side of this project rather than the programming side.

Abstract Algebra

The core mathematical tool behind this project is abstract algebra. Although I’m but a lowly incoming college freshman who hasn’t actually taken abstract algebra, I’ve learned a little bit about it on my own from the Internet, mainly due to my interest in Haskell and category theory.

As usual, Wikipedia summarizes this concept very nicely, in a completely non-self-referential and totally useful way:

Abstract algebra (occasionally called modern algebra) is the study of algebraic structures.

Wasn’t that helpful? To be fair, Wikipedia isn’t wrong, per se.

I view abstract algebra as a way to abstract concepts that appear in many different forms. For example, it lets us treat, say, the symmetries of regular polygons in the same way that we might treat the permutations of a Rubik’s cube. It allows us to describe certain behaviors and properties of these objects using a unified and powerful language.

So what does this have to do with music? Well, it turns out that polygons and Rubik’s cubes aren’t the only things that abstract algebra can describe—in fact, that’s far from the truth! A huge variety of concepts from a wide variety of disciplines lend themselves well to being studied under the lens of abstract algebra.

Music is no exception.

The Algebra of Music

Some Background

(If you are familiar with the notion of pitch classes, please feel free to skip ahead to the next section.)

Let’s think musically now. Music is made up of a series of notes, each having a particular frequency (or pitch) and duration. For now, I will focus primarily on pitch, but duration (and rhythm in general) is a very important topic that we will cover later!

Pitch is divided into two parts: a pitch class and an octave. Pitch classes are assigned letter names: A, B, C, D, E, F, or G. These are the white notes on a piano (see picture below). However, there are also pitch classes in between some of these letters! These correspond to the black notes on a piano. We represent them with either a “sharp” (symbolized ♯) or “flat” (symbolized ♭). A sharp raises a pitch by a half-step, and a flat lowers a pitch by a half-step (a half-step, or semitone, is simply the distance from one note on the piano to the next, regardless of color). So, for example, A♯ (“A sharp”) is the black note just to the right of A on the piano. This could also be notated B♭ (“B flat”). We say that A♯ and B♭ are enharmonic because they represent the same note.

A piano keyboard with note names. I will use the piano as a pedagogical tool
because of its easily-visualized keys, but these concepts apply to every pitched
instrument in standard Western music.

A piano keyboard with note names. I will use the piano as a pedagogical tool because of its easily-visualized keys, but these concepts apply to every pitched instrument in standard Western music.


One thing to watch out for is that E♯ is enharmonic to F, because E is right next to F on the piano (no black keys in between). Similarly, B♯ is enharmonic to C, because B is right next to C. The same logic applies to F♭ and C♭. This gives us a total of twelve possible pitches:

  1. A
  2. A♯/B♭
  3. B
  4. C
  5. C♯/D♭
  6. D
  7. D♯/E♭
  8. E
  9. F
  10. F♯/G♭
  11. G
  12. G♯/A♭

You’ll notice that I’ve listed twelve pitch classes, but you know as well as I do that there are many more than just twelve keys on a piano. Even in the picture above you can see way more than twelve keys! The reason for this is that this group of twelve pitches repeats itself all the way up and down the piano. So, in reality, there are actually multiple notes that we would call “C,” but each one is in a different octave. An octave is just grouping of the twelve pitch classes above. In the above picture, there are about two and a half octaves visible (C to C is one, to C again is two, and to the last G is about a half).

There is a very simple relationship between the frequencies of notes that differ by one octave: a note that is one octave higher than another note will have twice the frequency of that note. So, if we know that one C has a frequency of 440 Hz, we also know that 880 Hz and 220 Hz represent C as well. So while 220 Hz, 440 Hz, and 880 Hz are different pitches (they reside in different octaves), they are in the same pitch class, C. This simple relationship is why the human ear perceives all C’s to sound alike. This also explains why they are all grouped together in one pitch class!

(The more technical reason that all C’s sound so similar is that they share many of the same overtones. This is a direct consequence of the fact that they are all related in a simple 2:1 ratio.)

Put another way, the set of all C’s forms the pitch class, whereas one individual C in a particular octave (with a particular frequency) would be what we call a pitch. There is no frequency for the abstract notion of a pitch class (like C), but there is a frequency assigned to a note with a pitch class in an octave (like C4, the C in the fourth octave).

Operations on Pitch Classes

So, what can we do with these pitch classes? Well, one thing we could talk about would be some sort of a notion of distance between them. Looking at the picture of the piano (or our listing of pitch classes), it would be reasonable to say that, for example, the distance from A to A♯ is one semitone, the distance from A to B is two, and the distance from C to G is seven. Additionally, the distance from any note to itself should be zero.

Let’s define a function \( d \) (for distance) that determines how far you have to travel to get from one pitch class to another (in semitones). Using the above examples, we have:

We could extend this definition to account for signed distance, meaning that while the distance from C to G may be positive seven, the distance from G to C would be negative seven. We arrive at the following examples:

In general, we have the property that, for any notes \( x \) and \( y \), \( d(x, y) = -d(y, x) \). We also know that \( d(x, x) = 0 \). We’re off to a great start!

Another function that we might want to have is one that will give us a new pitch class if we add a particular signed distance (in semitones) to another pitch class. Let’s call this function \( a \) (for add). We have:

What properties do we have with this function? Glad you asked! Here are some that I spot:

Can you think of any others?

The properties that I have listed should actually make a lot of sense. In fact… they actually look really, really similar to some other operations that I am sure you are very familiar with!

We run into a little bit of trouble with our functions if we explore around the edges of our group of pitch classes. For example, what is \(a(G♯, 1)\)? There doesn’t seem to be anything “above” a G♯. However, remember that our pitch classes repeat themselves; one note above G♯ is A! Our pitch class “wraps around” and goes back to the first element. In fact, this is the same behavior as the hands on a clock! If you add two hours to eleven o’clock, you don’t end up with thirteen o’clock—the numbers wrap around and you are left with one o’clock. In mathematics, we call this modular arithmetic. We will return to this idea later.

To explore these operations and their properties more, let us first go back to our old friend, the set of integers, and see if we can make sense of these operations there.

Operations on Integers

Let’s first try to define \( d \) for the integers. It shouldn’t take much convincing to realize that the signed distance between two integers \( x \) and \( y \) is simply their difference, \( y - x \). Let’s try it out:

Our properties of \( d \) that we outlined before still hold! These properties are, in fact, more like laws in the sense that we define \( d \) by these properties, rather than discovering these properties about \( d \) after the fact. They are intuitive truths upon which we build our function.

This same notion of laws applies to our other function, \( a \). One readily apparent function over the integers that satisfies the laws of \( a \) is just ordinary addition:

And so, it seems as if, for the integers, \( d(x, y) = y - x \) and \( a(x, n) = x + n \).

Let us see if we can formalize this, and perhaps prove that ordinary subtraction and addition really do fit our laws for \( d \) and \( a \).

Formalizing Our Operations

Let us first focus on our \( a \) function. We are now ready to formally define exactly the laws that a function must follow if it wishes to serve as an \( a \) function. This will allow us to generalize our \( a \) function to pretty much anything we can think of, including music (which is how we got into this whole formalization business in the first place).

And so, because addition over the integers passes the above four tests, we can hereby decree it to be an official representative of the society of \(a\)! Woohoo!

In other words, addition follows all of the laws that we expect \(a\) to follow, and so if we were to define \(a\) over the integers, letting \(a(x, y) = x + y\) would be a perfectly valid thing to do.

This process will also work with real numbers and addition. It will also work with the positive real numbers and multiplication. Try working out \(a\) for different sets of numbers, or even seemingly non-mathematical things like food or crochet! The applications are limited only by your imagination. Sometimes you will find something that works (as in the case of addition over the integers), and other times you will find that it will not work (such as in the case of multiplication over the integers). Each time you try it out, the operation, identity element, and inverse elements will all be different. It’s very fun to try to stretch your brain to come up with ways to fit this mathematical abstraction to the real world, and in many cases, you will find the operation to be very intuitive in the end!

Speaking of real world applications… let’s get back to the music!

…But first, let’s talk about our original function, \( d \). It turns out that our definition of \(a\) is powerful enough to be able to define \(d\) in terms of \(a\). This means that we don’t need to come up with a new set of laws! We can rely on the fact that we proved \(a\) to be correct, and so we know that \(d\) has solid mathematical grounding. It’s quite a simple definition, but it uses the important fact that every element must have an inverse. We let \( d(x, y) = a(y, x^{-1})\), and we’re done. For integers, this means that \( d(x, y) = a(y, -x) = y - x\), which is exactly what we said before. We can now prove our properties about \(d\), too. For the case of integers, we have:

As you can see, \(d\) is redundant when we have the awesome power of \(a\).

Oh, by the way, you just did some of that abstract algebra stuff. Specifically, you just learned the basics of group theory. An operation that follows our \(a\) laws, combined with a set of objects (like the integers), forms a group. And that’s really, really cool. Integers under addition form a group, real numbers under multiplication form a group, rotations of polygons form a group, Rubik’s cube permutations form a group, “clock” (modular) arithmetic forms a group, and finally, we can of course, apply group theory to music. All of these can be described in terms of our all-powerful \(a\) laws (the \(a\) laws are formally called the group laws, by the way, but I hadn’t wanted to throw out the term group before now).

(By the way, if you want to be truly mind blown, look at this Math StackExchange post once you are comfortable with the notion of a group—which you should be after the next few sections. My mind figuratively exploded when I read this for the first time.)

Now let’s get back to the music!

Operations on Pitch Classes (Revisited)

We run into some issues if we try to formulate \(a\) as we did before: most egregiously, our definition of \(a\) for pitch classes takes in a pitch class and an integer—that’s not how groups work! Groups utilize one set, and one set only. We can’t deal with both pitch classes and integers at the same time; they are incompatible! Or are they? What if pitch classes were integers? Then we would only be dealing with one set: the integers (or the pitch classes, because saying “pitch classes” would be synonymous with saying “integers”).

But no, that’s ridiculous. There are an infinite number of integers, and only twelve pitch classes. But you know what else there is twelve of? Hours in a day. Coincidence? Yeah, pretty much… or is it?

Yeah, it is. However, we can borrow the idea of modular arithmetic from clocks and apply it here beautifully. Remember that our pitch classes behave just like the hands on a clock: one semitone above G♯ is A. In a sense, twelve (G♯) “equals” zero (A). In the case of the integers, we would say that twelve is equal to zero (modulo twelve). This is written out as \(12 \equiv 0\ (\text{mod}\ 12)\). We could also say that, for example, \(14 \equiv 2\ (\text{mod}\ 12)\) and \(5 \equiv 2\ (\text{mod}\ 3)\).

And so, we can create a group for integers from zero to twelve (or any upper bound) with the operation of “clock” (modular) arithmetic. You can verify that all the laws hold, if you wish. One interesting thing to take note of is the inverse element in this group; because there are no longer negative numbers, the inverse element must be something else. If you think about it, winding the clock back one hour is the same as moving it ahead by eleven. I’m sure you have had the experience of trying to set an old clock to a particular time, only to accidentally pass a certain number, which results in you having to increase all the way past twelve hours and go back to the number you wanted (it’s even worse for minutes, because you’re working modulo sixty). This is the concept of an inverse element working in real life—you’ve been working with inverse elements for longer than you’ve thought!

Now that we have a group for our clock numbers (zero through twelve), we are but one step away from creating a group for our pitch classes. In fact, we pretty much already did so! We mapped every pitch class to a clock number, so wherever we see a clock number, we can replace it with the corresponding pitch class, and vice versa. This one-to-one mapping is called an isomorphism in group theory. Look at you, learning all these fancy words for simple concepts!

And so, it is time to define our first proper operation on pitch classes. How exciting! Well, in reality, we’ll just be using our old clock arithmetic with a fancy new name. In pitchclassland, clock arithmetic becomes the new and shiny “pitch shift” operator! It is exactly our original definition of \(a\), but now with all the kinks worked out. For example:

Although it seems like we haven’t accomplished much, we actually have a lot of power in our hands now. We’ve rigorously created a mathematical formulation of music at the most basic level, the pitch class. The bulk of the work that we have done so far wasn’t necessarily in creating \ a \), but rather the mathematical foundation of any operation that we could apply.

What other operations can you define on the set of pitch classes? The sky is the limit!

Next, we shall explore another mathematical structure that appears within music: the monoid.


Monoids are actually really simple, especially compared to groups. Monoids are simply groups without inverse elements; that is, there doesn’t need to be an undo operation. So, every group is also a monoid, but not every monoid is a group. You can think of monoids as things that can “add,” but not “subtract.”

The canonical example of a monoid is string concatenation (addition): “abc” + “def” = “abcdef”. We can’t really “subtract” a string from this, so string concatenation forms a monoid, rather than a fully-fledged group.

That’s all! A monoid is a group without inverse elements; a group is a monoid with inverse elements.

Let us now explore what we can apply monoids to within music. Now that we know the theory behind some basic abstract algebra, we don’t have to go through the back-breaking effort of trying to understand the abstract concepts! We can just apply what we already know.

Parallel Composition Monoid (Chords)

We can define another operator, \( p \), that takes in two notes and returns the chord created when stacking them (for now, we will ignore note duration, but this approach will work with different durations too). I call this operator \( p \) because I like to think of it as a parallel composition operator (as opposed to sequential, which we will see later). We shall see that \( p \) forms a monoid over the set of all chords.

As an example usage of \(p\), if we wanted to play the notes C4 and E4 at the same time, we could construct this as \( p(C4, E4) \). This will return a chord. Note that \( p \) is not an operator over the set of all pitch classes (as \(a\) was), but rather an operator over the set of all musical objects (all notes, all chords, and as we will see later, all sequential compositions). Now, \(p\) will take in two musical objects and return a new musical object This allows us to construct chords with many more notes by repeatedly applying \(p\); for example, if we wanted a C major chord, we could write \( p(p(C4, E4), G4)\).

Let us quickly verify that \(p\) forms a monoid over the set of all chords:

Let’s see one more monoid.

Sequential Composition Monoid

We will define another operation, \(s\) to be the operation that composes two musical objects sequentially, one after another. You can verify the three monoid laws for yourself, if you’d like. What do you think the identity element would be?

(Hint: don’t forget that notes have a duration, too.)

Algebraic Operations on Melodies

And now, we can apply all of what we have learned to actual music. We have already defined two very important operators, parallel and sequential composition, that operate on sets of notes and chords (musical objects). Parallel composition will take two musical objects and layer them; sequential composition will take two musical objects and concatenate them. We can define many more such operators, like the reverse operator, \(r\). It takes in a single musical object and reverses it; a note reversed is just the note itself, a parallel composition reversed is all its components reversed, and a sequential composition reversed is the composition backward.

We can also generalize \(a\) to work on any musical object; chords can be shifted up and down, and so can melodies. In my project specifically, I allow for shifting up and down by either major intervals, minor intervals, or any (atonal) interval—this adjusts the “flavor” (tonality) of the randomly generated songs. You can think of many different algebraic operations on melodies, like changing note durations, inverting notes, repeating sequences, etc., but I will leave that up to you!

The key application of this theory in my project is that I define a set of a few of these operations, and randomly (and recursively) apply them to some starter melodies. This is a very flexible approach, as incredibly complex songs can be generated from simple melodies and operations.

In other words, the notes themselves of the song are not randomly generated; instead, the operations applied to these notes are randomly selected.

And that’s it! I believe that this approach provides an elegant and simple framework for manipulating melodies and musical objects that is easily extensible in many different ways. I encourage you to think about how you would do things differently, or how you could extend what I have done. Most importantly, have fun with it! It is music, after all: the mathematics of sense.